Leveraging Digital Devices for Objective Behavioral Health Assessment: Computational Machine Learning Methods for Sleep and Mental Health Evaluation

Anshika Arora (), Deepika Vatsa (), Aryan Srivastava, Harsh Vardhan Chauhan and Abhay Pratap
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Anshika Arora: Delhi Technological University
Deepika Vatsa: Bennett University
Aryan Srivastava: Bennett University
Harsh Vardhan Chauhan: Bennett University
Abhay Pratap: Bennett University

A chapter in Machine Learning and Deep Learning Modeling and Algorithms with Applications in Medical and Health Care, 2025, pp 347-367 from Springer

Abstract: Abstract Massive implementation of digital devices in life is raising concerns over their role in mental well-being. This research considers the correlation between technology usage and mental health by using a sample of 1000 people between 18 to 60 years old. Participants used self-report measures to track their daily time spent on screens, hours spent on social media sites, sleep quality, and numerous other markers of mental well-being. Advanced statistical methods and machine learning models such as Random Forest and Gradient Boosting Machines have been applied to identify and quantify cyberactivity effects. Results The study found some highly significant associations: hours on screen were positively associated with stress levels, (r = 0.67), and low-quality sleep, (r = − 0.42); in contrast, evening use was the worst. Machine learning models had a good predictive accuracy of (up to 85.7%). The most important predictors identified were screen time, hours of sleep, and stress. Clustering techniques unveiled distinct behavioral subgroups where the impact of digital use varies. The results further emphasize the importance of adopting positive digital practices, for example, avoiding exposure in the evening, to mitigate risks to long-term mental health. Although the research draws crucial insights for individuals, policymakers, and designers of information and communication technologies, they do have limitations, where their dependence on self-reports may lead to such bias. Longitudinal studies involving diverse populations remain critical in unraveling and exploring the complex relationship involving the use of technology within the increasingly digital environment.

Keywords: Digital devices; Mental health; Screen time; Sleep quality; Machine learning; Behavioral patterns (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:ssrchp:978-3-031-98728-1_17

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DOI: 10.1007/978-3-031-98728-1_17

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